Industrial-scale battery management involves various types of tasks, such as estimation, prediction, and system-level diagnostics. Each task employs distinct data across temporal scales, sensor resolutions, and data channels. Building task-specific methods requires a great deal of data and engineering effort, which limits the scalability of intelligent battery management. Here we present the Flexible Masked Autoencoder (FMAE), a flexible pretraining framework that can learn with missing battery data channels and capture inter-correlations across data snippets. FMAE learns unified battery representations from heterogeneous data and can be adopted by different tasks with minimal data and engineering efforts. Experimentally, FMAE consistently outperforms all task-specific methods across five battery management tasks with eleven battery datasets. On remaining life prediction tasks, FMAE uses 50 times less inference data while maintaining state-of-the-art results. Moreover, when real-world data lack certain information, such as system voltage, FMAE can still be applied with marginal performance impact, achieving comparable results with the best hand-crafted features. FMAE demonstrates a practical route to a flexible, data-efficient model that simplifies real-world multi-task management of dynamical systems.

Universal Differential Equations (UDEs), which blend neural networks with physical differential equations, have emerged as a powerful framework for scientific machine learning (SciML), enabling data-efficient, interpretable, and physically consistent modeling. In the context of smart grid systems, modeling node-wise battery dynamics remains a challenge due to the stochasticity of solar input and variability in household load profiles. Traditional approaches often struggle with generalization and fail to capture unmodeled residual dynamics. This work proposes a UDE-based approach to learn node-specific battery evolution by embedding a neural residual into a physically inspired battery ODE. Synthetic yet realistic solar generation and load demand data are used to simulate battery dynamics over time. The neural component learns to model unobserved or stochastic corrections arising from heterogeneity in node demand and environmental conditions. Comprehensive experiments reveal that the trained UDE aligns closely with ground truth battery trajectories, exhibits smooth convergence behavior, and maintains stability in long-term forecasts. These findings affirm the viability of UDE-based SciML approaches for battery modeling in decentralized energy networks and suggest broader implications for real-time control and optimization in renewable-integrated smart grids.

Uncrewed aerial systems have tightly coupled energy and motion dynamics which must be accounted for by onboard planning algorithms. This work proposes a strategy for coupled motion and energy planning using model predictive control (MPC). A reduced-order linear time-invariant model of coupled energy and motion dynamics is presented. Constrained zonotopes are used to represent state and input constraints, and hybrid zonotopes are used to represent non-convex constraints tied to a map of the environment. The structures of these constraint representations are exploited within a mixed-integer quadratic program solver tailored to MPC motion planning problems. Results apply the proposed methodology to coupled motion and energy utilization planning problems for 1) a hybrid-electric vehicle that must restrict engine usage when flying over regions with noise restrictions, and 2) an electric package delivery drone that must track waysets with both position and battery state of charge requirements. By leveraging the structure-exploiting solver, the proposed mixed-integer MPC formulations can be implemented in real time.

Demand for battery systems is increasing rapidly as efforts Prognosis (i.e., future prediction) in this framework is to decarbonise electricity grids and electrify mobility gather achieved using a separate model for the evolution of parameters pace [1]. Due to their long lifetime and high energy density, over battery lifetime, and this can range from a random Li-ion cells have become the workhorse in battery systems walk [8]-[10] to semi-empirical curve fits of trajectories that [2]. Although the cost of these has dramatically decreased in may be re-parameterised over lifetime using adaptive methods the last decade [3], the economics of storage needs to further such as particle filtering [13], [14], a Bayesian approach improve to increase take-up, notably in applications where that also provides parameter uncertainty estimates. Modeldriven battery systems are not yet competitive in terms of levelized approaches tend to use rather simple equivalent-circuit cost [4]. Also, given the risks of Li-ion cell demand outpacing models because they have relatively few parameters that need the supply of the required raw materials [5], it is crucial that to be fitted, whereas parameterising physics-based models, the performance of existing systems, especially in terms of such as those within the Doyle-Fuller-Newman framework lifetime, is maximised. A key element in improving the overall [15], [16], is plagued by poor identifiability [17]. This is cost-effectiveness of Li-ion batteries is accurate estimation mainly due to a lack of reference electrodes in commercial and prediction of battery state-of-health (SOH), which can cells which means that decoupling the positive and negative improve lifetime, warranty and insurance costs, system safety half-cell potentials is very difficult.

The usability of vehicles is highly dependent on their energy consumption. In particular, one of the main factors hindering the mass adoption of electric (EV), hybrid (HEV), and plug-in hybrid (PHEV) vehicles is range anxiety, which occurs when a driver is uncertain about the availability of energy for a given trip. To tackle this problem, we propose a machine learning approach for modeling the battery energy consumption. By reducing predictive uncertainty, this method can help increase trust in the vehicle's performance and thus boost its usability. Most related work focuses on physical and/or chemical models of the battery that affect the energy consumption. We propose a data-driven approach which relies on real-world datasets including battery related attributes. Our approach showed an improvement in terms of predictive uncertainty as well as in accuracy compared to traditional methods.

Recent surge in the number of Electric Vehicles have created a need to develop inexpensive energy-dense Battery Storage Systems. Many countries across the planet have put in place concrete measures to reduce and subsequently limit the number of vehicles powered by fossil fuels. Lithium-ion based batteries are presently dominating the electric automotive sector. Energy research efforts are also focussed on accurate computation of State-of-Charge of such batteries to provide reliable vehicle range estimates. Although such estimation algorithms provide precise estimates, all such techniques available in literature presume availability of superior quality battery datasets. In reality, gaining access to proprietary battery usage datasets is very tough for battery scientists. Moreover, open access datasets lack the diverse battery charge/discharge patterns needed to build generalized models. Curating battery measurement data is time consuming and needs expensive equipment. To surmount such limited data scenarios, we introduce few Deep Learning-based methods to synthesize high-fidelity battery datasets, these augmented synthetic datasets will help battery researchers build better estimation models in the presence of limited data. We have released the code and dataset used in the present approach to generate synthetic data. The battery data augmentation techniques introduced here will alleviate limited battery dataset challenges.

Accurate co-estimations of battery states, such as state-of-charge (SOC), state-of-health (SOH,) and remaining useful life (RUL), are crucial to the battery management systems to assure safe and reliable management. Although the external properties of the battery charge with the aging degree, batteries' degradation mechanism shares similar evolving patterns. Since batteries are complicated chemical systems, these states are highly coupled with intricate electrochemical processes. A state-coupled co-estimation method named Deep Inter and Intra-Cycle Attention Network (DIICAN) is proposed in this paper to estimate SOC, SOH, and RUL, which organizes battery measurement data into the intra-cycle and inter-cycle time scales. And to extract degradation-related features automatically and adapt to practical working conditions, the convolutional neural network is applied. The state degradation attention unit is utilized to extract the battery state evolution pattern and evaluate the battery degradation degree. To account for the influence of battery aging on the SOC estimation, the battery degradation-related state is incorporated in the SOC estimation for capacity calibration. The DIICAN method is validated on the Oxford battery dataset. The experimental results show that the proposed method can achieve SOH and RUL co-estimation with high accuracy and effectively improve SOC estimation accuracy for the whole lifespan.

I always have my phone within reach and I'm always paranoid about missing an urgent call or not answering a text in time. When I check it, I can't help but sneak a peek at the battery level to see if I need to bring my charger along or not. That said, I sometimes find that some battery levels are more recurrent than others. This led me to think more than once if there would be a way to model this frequency appearance. The purpose of this article is to present a probabilistic modeling basis to try and track the fluctuation of my battery level.

Electrical batteries are increasingly crucial in a variety of applications, from integration of intermittent energy sources with demand, to unlocking carbon-free power for the transportation sector through electric vehicles (EVs), trains and ships, to a host of advanced electronics and robotic applications. A key challenge however is that batteries degrade quickly with operating conditions. It is currently difficult to estimate battery health without interrupting the operation of the battery or without going through a lengthy procedure of charge-discharge that requires specialized equipment. In work recently published by Nature Machine Intelligence, researchers from the Smart Systems Group at Heriot-Watt University in Edinburgh, UK working together with researchers from the CALCE group at the University of Maryland in the US developed a new method to estimate battery health irrespective of operating conditions and battery design or chemistry, by feeding artificial intelligence (AI) algorithms with the raw battery voltage and current operational data. Darius Roman, the Ph.D. student that designed the AI framework said: "To date, the progress of data-driven models for battery degradation relies on the development of algorithms that carry out inference faster. Whilst researchers often spend a considerable amount of time on model or algorithm development, very few people take the time to understand the engineering context in which the algorithms are applied. By contrast, our work is built from the ground up. We first understand battery degradation through collaborations with the CALCE group at the University of Maryland, where in-house degradation testing of batteries was carried out. We then concentrate on the data, where we engineer features that capture battery degradation, we select the most important features and only then we deploy the AI techniques to estimate battery health."